Hi Everyone
I am Joost (pronounce ‘Yoast’) van Amersfoort (which is a Dutch city) and I am currently pursuing a PhD at the Unversity of Oxford under supervision of Professor Yarin Gal (in OATML) and Professor Yee Whye Teh (in OxCSML).
I am interested in variational inference (for example done through the reparametrisation trick, dropout and reversible models) and its applications such as uncertainty estimation, active learning and generative modelling.
Previously, I was a research engineer at Twitter Cortex, where I worked on large scale video models and recommendation systems. I did my masters at the University of Amsterdam, where I worked with Dr. Durk Kingma and Professor Max Welling. I've also done an internship at Facebook AI Research (FAIR).
Publications
Recent publications/preprints, for a full overview see my Google Scholar page.
-
BatchBALD: Efficient and Diverse Batch Acquisition for Deep
Bayesian Active Learning
Andreas Kirsch*, Joost van Amersfoort*, Yarin Gal
NeurIPS 2019, Blog, Code, BibTex@article{kirsch2019batchbald, title={BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning}, author={Kirsch, Andreas and van Amersfoort, Joost and Gal, Yarin}, booktitle={Advances in Neural Information Processing Systems}, year={2019} }
Reviewer
Open Source - projects and reproductions
For a full overview of all my open source work, see my Github account.
- FastMNIST: The default PyTorch MNIST dataset implementation is slow when used on a modern GPU. This replacement speeds up training by 2-3x.
- Glow in PyTorch: The code reproduces results from "Do Deep Generative Models Know What They Don't Know?" and includes a pretrained model, evaluation notebooks and training code.